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A data model organizes various data elements and standardizes their interrelationship and properties of real-world entities. Data modeling is, therefore, logically the process of creating these data models.
Data models consist of entities, and entities are the objects and concepts whose data we want to track. These, in turn, become the tables found in the database. Customers, products, manufacturers, and sellers are potential subjects.
Each entity has attributesdetails that users want to track. For example, the attribute is the name of the customer.
It goes without saying that if you want to ace your following job interview, you must first ensure your qualifications are worthy. But there's more you can do to help tip the odds in your favor. Knowing your qualification is essential, yes, but so is preparation.
This blog discusses being prepared for the questions you will likely encounter in an interview. Knowing what questions you will be asked, you can review the material and be equipped with the best answers.
So in this blog, we will discuss the most frequently asked data modeling interview questions. This blog covers test topics, data modeling scenarios, and more.
Top 24 Data Modeling Interview Questions and Answers
1. What do you understand by data modeling?
Generally, data models are created in the data analysis and design phase of the software development life cycle.
2. Explain different data models.
There are three data models conceptual, logical, and physical.
" The concept model shows the basic high-level design, while the physical data model shows a detailed view of the design. The conceptual model only shows entity names and entity relationships.
" The logical model displays each entity's entity names, entity relationships, attributes, primary keys, and foreign keys.
" The physical data model will display primary keys, foreign keys, table names, column names, and column data types. This view elaborates how the model will be implemented in the database.
3. What is a physical data model and physical data modeling?
The physical data model includes all required database tables, columns, relationships, and properties for the physical enactment of databases. Database performance, indexing method, and physical storage are important parameters of the physical model.
A database's basic or primary object is a table consisting of rows and columns. The strategy by which physical data models are created is called physical data modeling.
4. What is the difference between logical and physical data models?
When a data modeler works with a client, their title may be logical data modeler, physical data modeler, or a combination of the two.
A logical data modeler designs a data model to meet business requirements, creates and manages search data, compares data model versions, maintains a changelog, generates reports from the data model, and. In contrast, a physical data modeler must know about the properties of the source and target databases.
A physical data modeler should have the technical know-how to create data models from existing databases and debug data models with referential integrity, alternate keys, indexes, and how to map indexes to SQL code. It would be suitable for a physical data modeler to know about replication, clustering, etc.
5. What is a table (entity)?
Data stored in the form of rows and columns is called a table. Each column has a data type, and integrity constraints are enforced based on the situation.
6. What is a column (attribute)?
A column, also known as an array, is a vertical alignment of data and contains information related to that column.
7. What is a row?
A row, also known as a tuple or record, is a horizontal data alignment.
8. What is the difference between logical and physical data models?
Logical data model
" An analytical data model can logically design business requirements.
" He is responsible for his own realization of the data stored in the database.
" Contains entity, primary vital attributes, inverse keys, alternate key, rule, business relationship, definition, etc.
Physical data model
" The physical data model provides information about the target database resource and its properties.
" A physical data model helps you create a new database model and apply referential integrity constraints.
" A physical data model includes a table, key constraints, unique keys, columns, foreign keys, indexes, default values, etc.
9. What are the different types of restrictions?
Another type of constraint can be unique, null values, foreign keys, composite key or check constraint, etc.
10. What is a data modeling tool?
A data modeling tool is a software that helps create the flow of data and the relationship between data. Such tools include Borland Together, Altova Database Spy, casewise, Case Studio 2, etc.
11. What is a hierarchical DBMS?
In a hierarchical database, model data is organized in a tree structure. Information is stored in a hierarchical format. Data is represented using a parent-child relationship. In a hierarchical DBMS, parents can have many children, but children have only one parent.
12. What are the disadvantages of a hierarchical data model?
Disadvantages of a hierarchical data model are:
" It is not adjustable as it takes time to adapt to changing business needs.
" The structure deals with the issue of interdepartmental communication, vertical communication, and interdepartmental communication.
" A hierarchical data model can create inconsistency problems.
13. Explain the process-driven approach to data modeling
A process-driven approach in data modeling follows a step-by-step method regarding the relationship between the entity-relationship model and the organizational process.
14. What are the advantages of using data modeling?
The advantages of using data modeling in data warehouses are:
" It helps you manage business data by normalizing it and defining its attributes.
" Data modeling combines data from different systems to reduce data redundancy.
" Enables the creation of an effective database design.
" Data modeling helps the organizational department function as a team.
" Facilitates easy access to data.
15. What are the disadvantages of using data modeling?
The disadvantages of using data modeling are:
" It has less structural independence
" It can make the system complex.
16. Why are NoSQL databases more helpful than relational databases?
NoSQL databases have these advantages:
" They can keep structured, semi-structured, or unstructured data
" They have a dynamic schema, meaning they can evolve and change as quickly as needed
" NoSQL databases have sharding, the process of dividing and distributing data into smaller databases for faster access
" They offer failover and better recovery capabilities through replication
" It is easily scalable, growing or shrinking as needed
17. Explain data schema
A data schema is a schematic representation that illustrates data relationships and structures.
18. Explain the frequency of data collection
The data collection rate is the rate at which the data is collected. It also goes through different phases. These stages are:
1) extracting from multiple sources,
2) transformation,
3) cleaning and
4) storage.
19. What is database cardinality?
Cardinality is a numerical attribute of a relationship between two entities or entities.
20. What are the different types of principal relationships?
The different types of cardinal relationships are:
" One-to-One relationships
" One-to-Many relationships
" Multifaceted relationships
" Many too many relationships
21. Define a critical success factor and list its four types
A critical success factor is a favorable outcome of any activity required for an organization to achieve its objective.
The four types of critical success factors are:
" Industrial CSFs
" Strategic CSFs
" Environmental CSFs
" Temporary CSF
22. Should all databases be rendered in 3NF?
No, it is not an absolute requirement. However, denormalized databases are readily available, easier to maintain, and less redundant.
23. What is a junk dimension?
This grouping of low cardinality attributes, such as indicators and flags, was removed from other tables and then "discarded" into a notional dimension table. They are frequently used to initiate rapidly changing dimensions in data warehouses.
24. If a unique constraint is applied to a column, will an error be generated if you try to put two Null values into it?
No, it won't because the zero error values are never the same. You can put multiple null values in a column and not generate an error.
Conclusion
In this blog, we have seen the 24 most common data modeling interview questions.
